{"ID":5552806,"CreatedAt":"2026-07-02T01:54:51.863792489Z","UpdatedAt":"2026-07-03T20:14:26.82372516Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2607.00141","arxiv_id":"2607.00141","title":"AD-MPCC: Adaptive Differentiable Model Predictive Contouring Control for Autonomous Racing","abstract":"This paper presents Adaptive Differentiable Model Predictive Contouring Control (AD-MPCC), a framework for autonomous racing that integrates differentiable MPCC with online parameter estimation to handle varying road-surface conditions. For online parameter estimation, we leverage a parameterized Pacejka Magic Formula together with a regularized moving-horizon estimation scheme with exponentially decaying weights to capture road interactions and update parameters in real time. Furthermore, we propose a differentiable MPCC (Diff-MPCC) framework that enables optimal adjustment of objective weights based on predefined long-horizon performance costs. To implement Diff-MPCC for online objective weight adaptation, we propose a Pacejka-informed machine learning model that is trained in a supervised manner using data generated by Diff-MPCC to tune the objective weights. Simulation results demonstrate that AD-MPCC reliably ensures safety and achieves faster lap times compared to baseline controllers in both single-surface and multiple-surface scenarios.","short_abstract":"This paper presents Adaptive Differentiable Model Predictive Contouring Control (AD-MPCC), a framework for autonomous racing that integrates differentiable MPCC with online parameter estimation to handle varying road-surface conditions. For online parameter estimation, we leverage a parameterized Pacejka Magic Formula...","url_abs":"https://arxiv.org/abs/2607.00141","url_pdf":"https://arxiv.org/pdf/2607.00141v1","authors":"[\"Nam T. Nguyen\",\"Binh Nguyen\",\"Ahmad Amine\",\"Thanh Vo-Duy\",\"Rahul Mangharam\",\"Truong X. Nghiem\"]","published":"2026-06-30T20:19:35Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"eess.SY\"]","methods":"[]","has_code":false}